Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation
Interval type-2 fuzzy c-means (IT2FCM) clustering algorithm can describe more uncertainty than fuzzy c-means (FCM) clustering algorithm by using two fuzzifiers to construct a more inclusive boundary. How to obtain appropriate fuzzifiers and initialize cluster centers are essential tasks for the IT2F...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8715432/ |
| _version_ | 1819170130225004544 |
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| author | Feng Zhao Yilei Chen Hanqiang Liu Jiulun Fan |
| author_facet | Feng Zhao Yilei Chen Hanqiang Liu Jiulun Fan |
| author_sort | Feng Zhao |
| collection | DOAJ |
| description | Interval type-2 fuzzy c-means (IT2FCM) clustering algorithm can describe more uncertainty than fuzzy c-means (FCM) clustering algorithm by using two fuzzifiers to construct a more inclusive boundary. How to obtain appropriate fuzzifiers and initialize cluster centers are essential tasks for the IT2FCM. To effectively solve these problems, this paper proposes an alternate particle swarm optimization-based adaptive interval type-2 intuitionistic fuzzy c-means clustering algorithm (A-PSO-IT2IFCM) and applies this proposed method to color image segmentation. First, in order to further deal with the uncertainty, a novel interval type-2 fuzzy clustering objective function is constructed by utilizing the intuitionistic fuzzy information extracted from images. Then an alternate particle swarm optimization (PSO) scheme is designed to optimize fuzzifiers and cluster centers alternatively. In addition, a multiscale update strategy for the positions of particles is introduced into the A-PSO-IT2IFCM to increase the diversity of swarm and boost the convergence of optimization. The color image segmentation experiments on Berkeley and UC Merced Land Use datasets show that the proposed algorithm can adaptively determine fuzzifiers and cluster centers and achieve good segmentation results. |
| first_indexed | 2024-12-22T19:30:30Z |
| format | Article |
| id | doaj.art-7386539e62cf4d8281bd184e8aef2b74 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:30:30Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7386539e62cf4d8281bd184e8aef2b742022-12-21T18:15:08ZengIEEEIEEE Access2169-35362019-01-017640286403910.1109/ACCESS.2019.29168948715432Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image SegmentationFeng Zhao0https://orcid.org/0000-0002-0323-9573Yilei Chen1Hanqiang Liu2Jiulun Fan3Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an, ChinaKey Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an, ChinaKey Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an, ChinaInterval type-2 fuzzy c-means (IT2FCM) clustering algorithm can describe more uncertainty than fuzzy c-means (FCM) clustering algorithm by using two fuzzifiers to construct a more inclusive boundary. How to obtain appropriate fuzzifiers and initialize cluster centers are essential tasks for the IT2FCM. To effectively solve these problems, this paper proposes an alternate particle swarm optimization-based adaptive interval type-2 intuitionistic fuzzy c-means clustering algorithm (A-PSO-IT2IFCM) and applies this proposed method to color image segmentation. First, in order to further deal with the uncertainty, a novel interval type-2 fuzzy clustering objective function is constructed by utilizing the intuitionistic fuzzy information extracted from images. Then an alternate particle swarm optimization (PSO) scheme is designed to optimize fuzzifiers and cluster centers alternatively. In addition, a multiscale update strategy for the positions of particles is introduced into the A-PSO-IT2IFCM to increase the diversity of swarm and boost the convergence of optimization. The color image segmentation experiments on Berkeley and UC Merced Land Use datasets show that the proposed algorithm can adaptively determine fuzzifiers and cluster centers and achieve good segmentation results.https://ieeexplore.ieee.org/document/8715432/Image segmentationinterval type-2 fuzzy clusteringintuitionistic fuzzy setparticle swarm optimizationalternate optimization |
| spellingShingle | Feng Zhao Yilei Chen Hanqiang Liu Jiulun Fan Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation IEEE Access Image segmentation interval type-2 fuzzy clustering intuitionistic fuzzy set particle swarm optimization alternate optimization |
| title | Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation |
| title_full | Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation |
| title_fullStr | Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation |
| title_full_unstemmed | Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation |
| title_short | Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation |
| title_sort | alternate pso based adaptive interval type 2 intuitionistic fuzzy c means clustering algorithm for color image segmentation |
| topic | Image segmentation interval type-2 fuzzy clustering intuitionistic fuzzy set particle swarm optimization alternate optimization |
| url | https://ieeexplore.ieee.org/document/8715432/ |
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